Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05° × 0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.

Wetland methane (CH₄) emissions over the Boreal–Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002–2021) of Boreal–Arctic wetland CH₄ emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH₄ emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH₄ emissions (22.3 Tg CH₄ yr⁻¹) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH₄ emission driven by amplified Boreal–Arctic warming and greening.

Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄ budget. Data‐driven upscaling of CH₄ fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH₄ emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH₄ flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH₄ fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH₄ emissions of 146 ± 43 TgCH₄ y⁻¹ for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH₄ y⁻¹) and overlaps with top‐down atmospheric inversion models (155–200 TgCH₄ y⁻¹). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄ fluxes has the potential to produce realistic extra‐tropical wetland CH₄ emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Future changes in climate and socioeconomic systems will drive both the availability and use of water resources, leading to evolutions in scarcity. The contributions of both systems can be quantified individually to understand the impacts around the world, but also combined to explore how the coevolution of energy-water-land systems affects not only the driver behind water scarcity changes, but how human and climate systems interact in tandem to alter water scarcity. Here we investigate the relative contributions of climate and socioeconomic systems on water scarcity under the Shared Socioeconomic Pathways-Representative Concentration Pathways framework. While human systems dominate changes in water scarcity independent of socioeconomic or climate future, the sign of these changes depend particularly on the socioeconomic scenario. Under specific socioeconomic futures, human-driven water scarcity reductions occur in up to 44% of the global land area by the end of the century.